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Home Web Front-end JS Tutorial Building a Spam Email Classifier Using AI: A Basic Application

Building a Spam Email Classifier Using AI: A Basic Application

Dec 31, 2024 am 11:32 AM

Spam Email Classifier with Node.js

This project uses Node.js and the Natural library to create an AI-based application that classifies emails as spam or not spam. The application uses a Naive Bayes classifier for spam detection, which is a common algorithm for text classification tasks.

Prerequisites

Before you begin, make sure you have the following installed:

  • Node.js: Download Node.js
  • npm (Node Package Manager): npm comes with Node.js installation.

Steps to Set Up the Project

Step 1: Set Up Your Project

  1. Create a Project Folder: Open your terminal or command prompt and create a new folder for your project.
   mkdir spam-email-classifier
   cd spam-email-classifier
  1. Initialize a Node.js Project: Inside the folder, run the following command to create a package.json file.
   npm init -y

Step 2: Install Dependencies

Run the following command to install the required dependencies:

npm install natural
  • natural: A library that provides various NLP (Natural Language Processing) tools including classification using Naive Bayes.

Step 3: Create the Spam Classifier

Create a new JavaScript file (e.g., spamClassifier.js) and add the following code:

const natural = require('natural');

// Create a new Naive Bayes classifier
const classifier = new natural.BayesClassifier();

// Sample spam and non-spam data
const spamData = [
  { text: "Congratulations, you've won a 00 gift card!", label: 'spam' },
  { text: "You are eligible for a free trial, click here to sign up.", label: 'spam' },
  { text: "Important meeting tomorrow at 10 AM", label: 'not_spam' },
  { text: "Let's grab lunch this weekend!", label: 'not_spam' }
];

// Add documents to the classifier (training data)
spamData.forEach(item => {
  classifier.addDocument(item.text, item.label);
});

// Train the classifier
classifier.train();

// Function to classify an email
function classifyEmail(emailContent) {
  const result = classifier.classify(emailContent);
  return result === 'spam' ? "This is a spam email" : "This is not a spam email";
}

// Example of using the classifier to detect spam
const testEmail = "Congratulations! You have won a 00 gift card.";
console.log(classifyEmail(testEmail)); // Output: "This is a spam email"

// Save the trained model to a file (optional)
classifier.save('spamClassifier.json', function(err, classifier) {
  if (err) {
    console.log('Error saving classifier:', err);
  } else {
    console.log('Classifier saved successfully!');
  }
});

Step 4: Run the Classifier

To run the classifier, open a terminal and navigate to the project folder. Then, run the following command:

node spamClassifier.js

You should see an output similar to this:

This is a spam email
Classifier saved successfully!

Step 5: Load the Saved Classifier (Optional)

You can load the classifier model later to classify new emails. Here’s how to load the model and classify new emails:

const natural = require('natural');

// Load the saved classifier
natural.BayesClassifier.load('spamClassifier.json', null, function(err, classifier) {
  if (err) {
    console.log('Error loading classifier:', err);
  } else {
    // Classify a new email
    const testEmail = "You have won a free iPhone!";
    console.log(classifier.classify(testEmail)); // Output: 'spam' or 'not_spam'
  }
});

Step 6: Improve the Model (Optional)

To improve the accuracy of the spam classifier, you can:

  • Add more training data: Include more samples of spam and non-spam emails.
  • Experiment with different algorithms: Try other classification algorithms or models if Naive Bayes is not sufficient for your needs.
  • Use advanced techniques: Implement deep learning or neural networks for more complex classification tasks.

Step 7: (Optional) Integrate with Email System

If you want to send or receive emails from the app, you can use the Nodemailer library to send emails.

  1. Install Nodemailer:
   mkdir spam-email-classifier
   cd spam-email-classifier
  1. Send an Email (Example):
   npm init -y

Building a Spam Email Classifier Using AI: A Basic Application


Conclusion

This guide walked you through setting up an AI app using Node.js and Naive Bayes to classify emails as spam or not spam. You can expand this app by:

  • Adding more training data for better accuracy.
  • Using more advanced machine learning techniques.
  • Integrating the classifier into a web application or email system.

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